• A tensor factorization-based particle swarm optimization is proposed to directly dived large-scale decision space into small-scale sub-spaces, alleviating the dimensionality cures of large-scale problems. • An circular algorithm of sub-population is designed to asymptotically push the whole population to the true Pareto front. • The proposed tensor factorization-based model can be applied to other large-scale and multi-objective evolutionary algorithms. In this paper, a tensor factorization-based circular optimization algorithm of subpopulations is proposed to solve multi- and many-objective optimization problems with large-scale decision variables. Various intermediated procedures, such as variable analysis, clustering, and grouping, to divide large-scale decision variables are needed by most existing algorithms, seriously affecting the optimization performance. The tensor factorization, which is excellent in decomposition and dimensionality reduction of tensors, is originally introduced to the proposed algorithm to decompose the large-scale decision space into small-scale sub-spaces. Then an evolutionary strategy is designed to optimize these sub-populations by speed-constrained multi-objective particle swarm optimization circularly, searching for ideal solutions in each newly constructed lower-scale sub-space. Finally, tensor factorization is used to reconstruct the whole population for asymptotically optimizing the population by all the subpopulations. Using the proposed algorithm on some well-known multi-objective and many-objective benchmarks with 1000 decision variables, it outperformed other state-of-the-art algorithms both in solution quality and convergence rate. The main weakness of the proposed algorithm is the reconstruction error caused by the tensor factorization, which slows down the convergence rate of optimization. Hence our future work is to construct a constrained objective function with the restricted condition of the sparsity of the tensor, which affects the reconstruction error, and seek the solving algorithm. 

Tensor factorization-based particle swarm optimization for large-scale many-objective problems

In this paper, a tensor factorization-based circular optimization algorithm of subpopulations is proposed to solve multi- and many-objective optimization problems with large-scale decision variables. Various intermediated procedures, such as variable analysis, clustering, and grouping, to divide large-scale decision variables are needed by most existing algorithms, seriously affecting the optimization performance. The tensor factorization, which is excellent in decomposition and dimensionality reduction of tensors, is originally introduced to the proposed algorithm to decompose the large-scale decision space into small-scale sub-spaces. Then an evolutionary strategy is designed to optimize these sub-populations by speed-constrained multi-objective particle swarm optimization circularly, searching for ideal solutions in each newly constructed lower-scale sub-space. Finally, tensor factorization is used to reconstruct the whole population for asymptotically optimizing the population by all the subpopulations. Using the proposed algorithm on some well-known multi-objective and many-objective benchmarks with 1000 decision variables, it outperformed other state-of-the-art algorithms both in solution quality and convergence rate. The main weakness of the proposed algorithm is the reconstruction error caused by the tensor factorization, which slows down the convergence rate of optimization. Hence our future work is to construct a constrained objective function with the restricted condition of the sparsity of the tensor, which affects the reconstruction error, and seek the solving algorithm.

Investigation of human trust by identifying stimulated brain regions using electroencephalogram

Human brain effective connectivity characterizes the causal effects of neural activities among different brain regions. Studies of brain effective connectivity networks (ECNs) for different populations contribute significantly to the understanding of the pathological mechanism associated with neuropsychiatric diseases and facilitate finding new brain network imaging markers for the early diagnosis and evaluation for the treatment of cerebral diseases. A deeper understanding of brain ECNs also greatly promotes brain-inspired artificial intelligence (AI) research in the context of brain-like neural networks and machine learning. Thus, how to picture and grasp deeper features of brain ECNs from functional magnetic resonance imaging (fMRI) data is currently an important and active research area of the human brain connectome. In this survey, we first show some typical applications and analyze existing challenging problems in learning brain ECNs from fMRI data. Second, we give a taxonomy of ECN learning methods from the perspective of computational science and describe some representative methods in each category. Third, we summarize commonly used evaluation metrics and conduct a performance comparison of several typical algorithms both on simulated and real datasets. Finally, we present the prospects and references for researchers engaged in learning ECNs. 

A Survey on Brain Effective Connectivity Network Learning

Effective Connectivity in Individuals with Alzheimer's Disease and Mild Cognitive Impairment: A Systematic Review

Identifying brain effective connectivity (EC) networks from neuroimaging data has become an effective tool that can evaluate normal brain functions and the injuries associated with neurodegenerative diseases. So far, there are many methods used to identify EC networks. However, most of the research currently focus on learning EC networks from single modal imaging data such as functional magnetic resonance imaging (fMRI) data. This paper proposes a new method, called ACOEC-FD, to learn EC networks from fMRI and diffusion tensor imaging (DTI) using ant colony optimization (ACO). First, ACOEC-FD uses DTI data to acquire some positively correlated relations among regions of interest (ROI), and takes them as anatomical constraint information to effectively restrict the search space of candidate arcs in an EC network. ACOEC-FD then achieves multi-modal imaging data integration by incorporating anatomical constraint information into the heuristic function of probabilistic transition rules to effectively encourage ants more likely to search for connections between structurally connected regions. Through simulation studies on generated datasets and real fMRI-DTI datasets, we demonstrate that the proposed approach results in improved inference results on EC compared to some methods that only used fMRI data.

/pdf/acoec-fd-ant-colony-optimization-for-learning-brain-1hsn6xes46.pdf

Aixiao Zou

Papers

ACOEC-FD: Ant Colony Optimization for Learning Brain Effective Connectivity Networks From Functional MRI and Diffusion Tensor Imaging.